Convolutional neural network-based computer-aided diagnosis in Hiesho (cold sensation)

Abstract

Hiesho (cold sensation) is a worldwide health problem primarily occurring in women. Females who suffered from Hiesho reported cold feeling at the extremities, which was also related to other chronic diseases. However, the diagnosis of Hiesho is still controversial because it depends on subjective approaches such as questionnaires. Quantitative and automatic Hiesho diagnosis is expected to increase diagnostic accuracy and lower the burden on patients and doctors. Following our previous study, which found that the temperature difference between females’ foreheads and plantar soles was significant in Hiesho patients, it was considered that training a convolutional neural network (CNN) with thermographic images can contribute to a computer-aided diagnosis (CAD) for Hiesho. Thus, this study proposes a CNN-based Hiesho CAD system. A total of 5612 thermographic images from 46 subjects (23 Hiesho patients and 23 healthy subjects) were used to train AlexNet, and the performance of the proposed CNN model was evaluated and compared with other machine learning-based models using accuracy, precision, sensitivity, specificity, and F1 score. The experimental results showed that the proposed CNN-based Hiesho CAD model had the highest performance (100%) for all evaluated items. In addition, it was concluded that thermographic images showed high feasibility for discriminating Hiesho, and CNN-based CAD showed high accuracy and reliability for automatic Hiesho diagnosis.